Local Interaction Autoregressive Model for High Dimension Time Series Data

High-dimensional matrix and tensor time series often exhibit local dependency, where each entry interacts mainly with a small neighborhood.

Accounting for local interactions in a prediction model can greatly reduce the dimensionality of the parameter space, leading to more efficient inference and more accurate predictions.

We propose a Local Interaction Autoregressive (LIAR) framework and study Separable LIAR, a variant with shared row and column components, for high-dimensional matrix/tensor time series forecasting problems.

We derive a scalable parameter estimation algorithm via parallel least squares with a BIC-type neighborhood selector.

Theoretically, we show consistency of neighborhood selection and derive error bounds for kernel and auto-covariance estimation.

Numerical simulations show that the BIC selector recovers the true neighborhood with high success rates, the LIAR achieves small estimation errors, and the forecasts outperform matrix time-series baselines.

In real data applications, a Total Electron Content (TEC) case study shows the model can identify localized spatio-temporal propagation and improved prediction as compared with non-local time series prediction models.